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An insect trap constructed using three-dimensional (3D) printing technology was tested in potato (Solanum tuberosum Linnaeus; Solanaceae) fields to determine whether it could substitute for the standard yellow sticky card used to monitor Bactericera cockerelli (Šulc) (Hemiptera: Psylloidea: Triozidae). Sticky cards have shortcomings that prompted search for a replacement: cards are messy, require weekly replacement, are expensive to purchase, and accumulate large numbers of nontarget insects. Bactericera cockerelli on sticky cards also deteriorate enough that specimens cannot be tested reliably for the presence of vectored plant pathogens. A prototype trap constructed using 3D printing technology for monitoring Diaphorina citri Kuwayama (Hemiptera: Psylloidea: Liviidae) was tested for monitoring B. cockerelli. The trap was designed to attract B. cockerelli visually to the trap and then funnel specimens into preservative-filled vials at the trap bottom. Prototype traps were paired against yellow sticky cards at multiple fields to compare the captures of B. cockerelli between cards and traps. The prototype trap was competitive with sticky cards early in the growing season when B. cockerelli numbers were low. We estimated that two or three prototype traps would collect as many B. cockerelli as one sticky card under these conditions. Efficacy of the prototype declined as B. cockerelli numbers increased seasonally. The prototype trap accumulated nontarget taxa that are common on sticky cards (especially Thysanoptera and Diptera), and was also found to capture taxa of possible interest in integrated pest management research, including predatory insects, parasitic Hymenoptera, and winged Aphididae (Hemiptera), suggesting that the traps could be useful outside of the purpose targeted here. We believe that 3D printing technology has substantial promise for developing monitoring tools that exploit behavioural traits of the targeted insect. Ongoing work includes the use of this technology to modify the prototype, with a focus on making it more effective at capturing psyllids and less susceptible to capture of nontarget species.
A compilation of mean values of the oxygen-isotope ratio relative to
standard mean ocean water (δ18O, in ‰) for 22 siIes
representative of conditions in northeastern Canada is complemented with
data on mean annual surface temperature, latitude, surface elevation, and
mean annual shortest distance to open ocean denoted by the 10% sea-ice
concentration boundary. Stepwise regression analysis is used to develop a
multivariate model suitable to inter the distribution of δ18O in
an area of complex topography and possibly mixed sourcing of advected water
vapor. The best model is produced by a run in the backward mode at the 95%
confidence level in which only temperature, latitude and distance to the
open ocean remain in the model (the correlation coefficient is 0.915, the
adjusted coefficient of determination is 0.809, the root mean square
residual is 1.62). This model is similar to the best δ18O
predictive model derived elsewhere for Greenland, suggesting a common
principal source of advected moisture.
Adler et al. (2016) open with a summary of the business case driving our field to change and close by providing principles for accomplishing that change, where they conclude that “there is no right answer to the ratings question” (p. 244). Lying between the opening and closing sections is a series of arguments for and against today's performance rating status quo, arguments illustrating just what happens when too many years are spent seeking answers along too narrow a path. In this commentary, we provide additional support for the strategy- and outcome-driven approach to performance management advocated in the article. In addition, we offer ideas for what has contributed to getting us and keeping us where we are. Unless we understand what has driven performance ratings research and practice to be the object of an intense and lengthy debate, these same forces may well drive us to carry out years-long experiments of questionable value along similarly narrow paths. We want to offer our views on how to foster outcome-based practice more broadly.
This manual presents solutions to all exercises from
Actuarial Mathematics for Life Contingent Risks (AMLCR), by David C.M. Dickson, Mary R. Hardy, Howard R. Waters, Cambridge University Press, 2009 ISBN 9780521118255
It should be read in conjunction with the spreadsheets posted at the website www.cambridge.org\97811ø76ø8443 which contain details of the calculations required. However, readers are encouraged to construct their own spreadsheets before looking at the authors' approach. In the manual, exercises for which spreadsheets are posted are indicated with anE.
From time to time, updates to this manual may appear at www.cambridge.org\ 97811ø76ø8443.
3.1 Figures S3.1, S3.2 and S3.3 are graphs of μx, lx and dx, respectively, as functions of age x up to x = 100. Each graph has been drawn using the values from ELT 15, Males and Females.
(a) The key feature of Figure S3.1 is that the value of μx is very low until around age 55, from where it increases steeply. Numerically, μx is very close to qx provided qx is reasonably small, so that the features in Figure S3.1 are very similar to those shown in Figure 3.1 in AMLCR. The features at younger ages show up much better in Figure 3.1 in AMLCR because the y-axis there is on a logarithmic scale. Note that the near-linearity in Figure 3.1 in AMLCR for ages above 35 is equivalent to the near-exponential growth we observe in Figure S3.1.
(b) The key feature of Figure S3.2 is that, apart from a barely perceptible drop in the first year due to mortality immediately following birth, the graph is more or less constant until around age 55 when it starts to fall at an increasing rate before converging towards zero at very high ages. This reflects the pattern seen in Figure S3.1.
(c) The function dx is the expected number of deaths between exact ages x and x + 1 out of l0 lives aged 0. The relatively high mortality in the first year of life shows clearly in Figure S3.3, as does the increase in the expected number of deaths for males in the late teenage years – the so-called ‘accident hump’.
1.1 The insurer will calculate the premium for a term or whole life insurance policy assuming that the policyholder is in relatively good health; otherwise, if the insurer assumed that all purchasers were unhealthy, the cost of insurance would be prohibitive to those customers who are healthy. The assumption then is that claims will be relatively rare in the first few years of insurance, especially since most policies are sold to lives in their 30s and 40s.
This means that the price is too low for a life who is very unwell, for whom the risk of a claim shortly after purchase might be 10 or 100 times greater than for a healthy life. The insurer therefore needs evidence that the purchaser is in good health, to avoid the risk that insurance is bought too cheaply by lives who have a much higher probability of claim.
The objective of underwriting is to produce a relatively homogeneous insured population when policies are issued. The risk that the policyholder purchases the insurance because they are aware that their individual risk is greater than that of the insured population used to calculate the premium, is an example of adverse selection risk. Underwriting is a way of reducing the impact of adverse selection for life insurance.
Adverse selection for an annuity purchaser works in the other direction – a life might buy an annuity if they considered their mortality was lighter than the general population.
This must-have manual provides solutions to all exercises in Dickson, Hardy and Waters' Actuarial Mathematics for Life Contingent Risks, the groundbreaking text on the modern mathematics of life insurance that is the required reading for the SOA Exam MLC and also covers more or less the whole syllabus for the UK Subject CT5 exam. The more than 150 exercises are designed to teach skills in simulation and projection through computational practice, and the solutions are written to give insight as well as exam preparation. Companion spreadsheets are available for free download to show implementation of computational methods.